Address reading systems have the task of recognizing the characters and numbers from the text elements located on items of mail, such as letters or packages, specifically from the area of the addressee's address, and of deriving sorting information from among them. For this purpose, a series of recognition methods are used, which after scanning of the item analyze the resultant electronic image step-by-step and classify its elements, in order finally to produce the information relevant for sorting in the form of electronic text. The substeps involved are, specifically, layout analysis (recognition of the type of item, determination of the regions of interest (ROI), segmentation of the text image into lines, words and/or characters), character classification or identification of joined-up handwriting, and final interpretation of the text elements.
The functional operation of each recognition method is controlled by parameters. Many of these parameters can be appraised/learned/trained by means of predetermined mathematical optimization criteria during a learning phase; they are referred to hereafter as trainable parameters. For all the other parameters, referred to hereafter as free parameters, there are no such optimization criteria. Both for the appraisal of trainable parameters and for the setting of free parameters, what are known as random samples are required from items taken as examples. Each random sample element is made up of a detail of the item image and the required correct solution (label, desired result). There are random samples for the address reading system as a whole, which are made up in each case of a complete item image and the sorting information. There are also random samples for the individual recognition methods (method-specific samples), which are made up in each case of the input image of the recognition method and its required output. A distinction is made between learning samples and test samples: learning samples are required for the appraisal of trainable parameters. Test samples serve for the evaluation of the performance of the trained recognition method. One possibility for the suitable setting of free parameters is the repeated evaluation of the performance of the recognition method with different parameter values. This operation is also referred to as optimization of the free parameters, even if the prime concern is not to find the global optimum in the mathematical sense but to achieve a good recognition performance within a limited time.
The state of the art is that free parameters of address reading systems are set by manual, repeated trial and error. The various parameter settings are assessed by the developer partly using heuristic criteria, partly using evaluations of the respective recognition method on method-specific test samples and partly using evaluations of the reading system as a whole on test samples. In addition, there are individual published papers in the area of pattern recognition systems which are aimed at an automation of this process. They use mathematical optimization methods for setting selected free parameters, the theory of which is described for example in [Press et al.: Numerical Recipes in C, Cambridge University Press, 1992], [D. E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, 1989], [I. Rechenberg, Evolutionsstrategien [evolution strategies] '94, Frommann-Holzboog, 1994]. Special evolutionary/genetic algorithms are often used in this case. In the general article [Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey, Y.-K. Wang and K.-C. Fan, in Proceedings of the International Conference on Pattern Recognition, 1996], a distinction is made between three types of problems: firstly the optimization of the selection of characteristics, secondly the optimization of the classification function and thirdly the optimization of the classification learning. Further papers on genetic optimization of pattern recognition systems can be found in the special issue [Pattern Recognition Letters: Special Issue on Genetic Algorithms, Vol. 16, No. 8, August 1995]. The procedure adopted in these publications can be summarized as follows:    1. choose one or more different settings of the free parameters, taking already evaluated parameter settings into account,    2. evaluate the parameter setting(s) on the basis of a test sample,    3. if the performance aimed for is achieved, then END, otherwise go to 1.
Transferred to a recognition method within an address reading system, this would mean that the method would have to be completely trained for each evaluation of a parameter setting in order for it then to be possible to measure its performance on a test sample.
The individual recognition methods in an address reading system are distinguished by the fact that large learning samples are necessary for their training. It is not uncommon for the complex calculations during training to take several hours on modern workstation computers. The technique described above of using mathematical optimization methods is consequently only suitable in a very qualified sense for the optimization of free parameters of address reading methods, since it would, on average, take many months of computing time to achieve good results.